Internet Server ClustersInternet Server Clusters

Jeff Chase

Duke University, Department of Computer Science

CPS 212: Distributed Information Systems

Using Clusters for Scalable ServicesUsing Clusters for Scalable Services

Clusters are a common vehicle for improving scalability and availability at a single service site in the network.

Are network services the “Killer App” for clusters?

• incremental scalability

just wheel in another box...

• excellent price/performance

high-end PCs are commodities: high-volume, low margins

• fault-tolerance

“simply a matter of software”

• high-speed cluster interconnects are on the market

SANs + Gigabit Ethernet...

cluster nodes can coordinate to serve requests w/ low latency

• “shared nothing”

[Fox/Brewer]: SNS, TACC, and All That[Fox/Brewer]: SNS, TACC, and All That

[Fox/Brewer97] proposes a cluster-based reusable software infrastructure for scalable network services (“SNS”), such as:

• TranSend: scalable, active proxy middleware for the Web

think of it as a dial-up ISP in a box, in use at Berkeley

distills/transforms pages based on user request profiles

• Inktomi/HotBot search engine

core technology for Inktomi Inc., today with $15B market cap.

“bringing parallel computing technology to the Internet”

Potential services are based on Transformation, Aggregation, Caching, and Customization (TACC), built above SNS.

TACCTACC

Vision: deliver “the content you want” by viewing HTML content as a dynamic, mutable medium.

1. Transform Internet content according to:

• network and client needs/limitations

e.g., on-the-fly compression/distillation [ASPLOS96], packaging Web pages for PalmPilots, encryption, etc.

• directed by user profile database

2. Aggregate content from different back-end services or resources.

3. Cache content to reduce cost/latency of delivery.

4. Customize (see Transform)

TranSend StructureTranSend Structure

$

$

$

FrontEnds

ProfilesControlPanel

html gif jpg

To Internet

SAN (high speed)Utility (10baseT)Coordination bus

$ Cache partition

... Datatype-specific distiller

[adapted from Armando Fox (through http://ninja.cs.berkeley.edu/pubs)]

SNS/TACC PhilosophySNS/TACC Philosophy

1. Specify services by plugging generic programs into the TACC framework, and compose them as needed.

sort of like CGI with pipes

run by long-lived worker processes that serve request queues

allows multiple languages, etc.

2. Worker processes in the TACC framework are loosely coordinated, independent, and stateless.

ACID vs. BASE

serve independent requests from multiple users

narrow view of a “service”: one-shot readonly requests, and stale data is OK

3. Handle bursts with designated overflow pool of machines.

TACC ExamplesTACC Examples

HotBot search engine

• Query crawler’s DB

• Cache recent searches

• Customize UI/presentation

TranSend transformation proxy

• On-the-fly lossy compression of inline images (GIF, JPG, etc.)

• Cache original & transformed

• User specifies aggressiveness, “refinement” UI, etc.

C TT

$$AA

TT

$$

C

DBDB

htmlhtml

[Fox]

(Worker) Ignorance Is Bliss(Worker) Ignorance Is Bliss

What workers don’t need to know

• Data sources/sinks

• User customization (key/value pairs)

• Access to cache

• Communication with other workers by name

Common case: stateless workers

C, Perl, Java supported

• Recompilation often unnecessary

• Useful tasks possible in <10 lines of (buggy) Perl

[Fox]

QuestionsQuestions

1. What are the research contributions of the paper?system architecture decouples SNS concerns from content

TACC programming model composes stateless worker modules

validation using two real services, with measurements

How is this different from clusters for parallel computing?

2. How is this different from clusters for parallel computing?

3. What are the barriers to scale in SNS/TACC?

4. How are requests distributed to caches, FEs, workers?

5. What can we learn from the quantitative results?

6. What about services that allow client requests to update shared data?e.g., message boards, calendars, mail,

SNS/TACC Functional IssuesSNS/TACC Functional Issues

1. What about fault-tolerance?

• Service restrictions allow simple, low-cost mechanisms.

Primary/backup process replication is not necessary with BASE model and stateless workers.

• Uses a process-peer approach to restart failed processes.

Processes monitor each other’s health and restart if necessary.

Workers and manager find each other with “beacons” on well-known ports.

2. Load balancing?

• Manager gathers load info and distributes to front-ends.

• How are incoming requests distributed to front-ends?

Porcupine: A Highly Available Porcupine: A Highly Available Cluster-based Mail ServiceCluster-based Mail Service

Yasushi Saito

Brian Bershad

Hank Levy

University of Washington Department of Computer Science and Engineering,

Seattle, WA

http://porcupine.cs.washington.edu/

[Saito]

Why Email?Why Email?

Mail is importantReal demand

Mail is hard

Write intensive

Low locality

Mail is easy

Well-defined API

Large parallelism

Weak consistency

[Saito]

How much of Porcupine isreusable to other services?

Can we use the SNS/TACCframework for this?

GoalsGoals

Use commodity hardware to build a large, scalable mail service

Three facets of scalability ...

Performance: Linear increase with cluster size

Manageability: React to changes automatically

Availability: Survive failures gracefully

[Saito]

Conventional Mail SolutionConventional Mail Solution

Static partitioning

Performance problems:

No dynamic load balancing

Manageability problems:

Manual data partition decision

Availability problems:

Limited fault tolerance

SMTP/IMAP/POP

Bob’smbox

Ann’smbox

Joe’smbox

Suzy’smbox

NFS servers

[Saito]

Key Techniques and RelationshipsKey Techniques and Relationships

Functional Homogeneity“any node can perform any task”

AutomaticReconfiguration

Load BalancingReplication

Manageability PerformanceAvailability

Framework

Techniques

Goals

[Saito]

Porcupine ArchitecturePorcupine Architecture

Node A ...Node B Node Z...

SMTPserver

POPserver

IMAPserver

Mail mapMailbox storage

User profile

Replication Manager

Membership Manager

RPC

Load Balancer

User map

[Saito]

Porcupine OperationsPorcupine Operations

Internet

A B...

A

1. “send mail to bob”

2. Who manages bob? A

3. “Verify bob”

5. Pick the best nodes to store new msg C

DNS-RR selection

4. “OK, bob has msgs on C and D 6. “Store

msg”B

C

Protocol handling

User lookup

Load Balancing

Message store

...C

[Saito]

Basic Data StructuresBasic Data Structures“bob”

BCACABAC

bob: {A,C}ann: {B}

BCACABAC

suzy: {A,C} joe: {B}

BCACABAC

Apply hash function

User map

Mail map/user info

Mailbox storage

A B C

Bob’s MSGs

Suzy’s MSGs

Bob’s MSGs

Joe’s MSGs

Ann’s MSGs

Suzy’s MSGs

[Saito]

fragment list

mailbox fragments

Porcupine AdvantagesPorcupine Advantages

Advantages:

Optimal resource utilization

Automatic reconfiguration and task re-distribution upon node failure/recovery

Fine-grain load balancing

Results:

Better Availability

Better Manageability

Better Performance

[Saito]

AvailabilityAvailability

Goals:Maintain function after failuresReact quickly to changes regardless of cluster sizeGraceful performance degradation / improvement

Strategy: Two complementary mechanisms

Hard state: email messages, user profile Optimistic fine-grain replication

Soft state: user map, mail map Reconstruction after membership change

[Saito]

Soft-state ReconstructionSoft-state Reconstruction

B C A B A B A C

bob: {A,C}

joe: {C}

B C A B A B A C

B A A B A B A B

bob: {A,C}

joe: {C}

B A A B A B A B

A C A C A C A C

bob: {A,C}

joe: {C}

A C A C A C A C

suzy: {A,B}

ann: {B}

1. Membership protocolUsermap recomputation

2. Distributed disk scan

suzy:

ann:

Timeline

A

B

ann: {B}

B C A B A B A C

suzy: {A,B}

Cann: {B}

B C A B A B A C

suzy: {A,B}ann: {B}

B C A B A B A C

suzy: {A,B}

[Saito]

suzy

ann

How does Porcupine React to How does Porcupine React to Configuration Changes?Configuration Changes?

300

400

500

600

700

0 100 200 300 400 500 600 700 800Time(seconds)

Messages/second

No failure

One nodefailureThree nodefailuresSix nodefailures

Nodes fail

New membership determined

Nodes recover

New membership determined

[Saito]

Hard-state ReplicationHard-state Replication

Goals:

Keep serving hard state after failures

Handle unusual failure modes

Strategy: Exploit Internet semantics

Optimistic, eventually consistent replication

Per-message, per-user-profile replication

Efficient during normal operation

Small window of inconsistency

[Saito]

How will Porcupine behave in a partition failure?

More on Porcupine ReplicationMore on Porcupine Replication

To add/delete/modify a message:• Find and update any replica of the mailbox fragment.

Do whatever it takes: make a new fragment if necessary...pick a new replica if chosen replica does not respond.

• Replica asynchronously transmits updates to other fragment replicas.

continuous reconciling of replica states

• Log/force pending update state, and target nodes to receive update.

on recovery, continue transmitting updates where you left off

• Order updates by loosely synchronized physical clocks.

Clock skew should be less than the inter-arrival gap for a sequence of order-dependent requests...use nodeID to break ties.

• How many node failures can Porcupine survive? What happens if nodes fail “forever”?

How Efficient is Replication?How Efficient is Replication?

0

100

200

300

400

500

600

700

800

0 5 10 15 20 25 30Cluster size

Me

ss

ag

es

/se

co

nd

Porcupine no replication

Porcupine with replication=2

68m/day

24m/day

[Saito]

How Efficient is Replication?How Efficient is Replication?

0

100

200

300

400

500

600

700

800

0 5 10 15 20 25 30Cluster size

Me

ss

ag

es

/se

co

nd

Porcupine no replication

Porcupine with replication=2

Porcupine with replication=2, NVRAM

68m/day

24m/day33m/day

[Saito]

Load balancing: Deciding where to store messagesLoad balancing: Deciding where to store messages

Goals:

Handle skewed workload well

Support hardware heterogeneity

No voodoo parameter tuning

Strategy: Spread-based load balancing

Spread: soft limit on # of nodes per mailbox

Large spread better load balance

Small spread better affinity

Load balanced within spread

Use # of pending I/O requests as the load measure

[Saito]

QuestionsQuestions• How to select the front-end node to handle the request? Does it

matter which one we choose?

• Don’t we already know how to build big mail servers? (e.g., Earthlink, Christenson USITS97) Why do we need Porcupine?

• What properties of the mail “data model” allow this approach, with weaker consistency guarantees than a database?

• How does the system leverage/exploit the weaker semantics?

• Can the architecture accommodate new features, e.g., Pachyderm-like storage/indexing of large mail collections?

• Could I run Porcupine on the same cluster with other applications?

• Could this have been built on Microsoft’s MSCS? How much application effort would have been saved?

Clusters: A Broader ViewClusters: A Broader View

MSCS (“Wolfpack”) is designed as basic infrastructure for commercial applications on clusters.

• “A cluster service is a package of fault-tolerance primitives.”

• Service handles startup, resource migration, failover, restart.

• But: apps may need to be “cluster-aware”.

Apps must participate in recovery of their internal state.

Use facilities for logging, checkpointing, replication, etc.

• Service and node OS supports uniform naming and virtual environments.

Preserve continuity of access to migrated resources.

Preserve continuity of the environment for migrated resources.

Wolfpack: ResourcesWolfpack: Resources

• The components of a cluster are nodes and resources.

Shared nothing: each resource is owned by exactly one node.

• Resources may be physical or logical.

Disks, servers, databases, mailbox fragments, IP addresses,...

• Resources have types, attributes, and expected behavior.

• (Logical) resources are aggregated in resource groups.

Each resource is assigned to at most one group.

• Some resources/groups depend on other resources/groups.

Admin-installed registry lists resources and dependency tree.

• Resources can fail.

cluster service/resource managers detect failures.

Fault-Tolerant Systems: The Big PictureFault-Tolerant Systems: The Big Picture

messaging system

file/storage system

database mail service cluster service

application service

application service

redundant hardwareparityECC

replicationRAID parity

checksumack/retransmission

replicationlogging

checkpointingvoting

replicationlogging

checkpointingvoting

Note:dependenciesredundancy at any/each/every levelwhat failure semantics to the level above?

Wolfpack: Resource Placement and MigrationWolfpack: Resource Placement and Migration

The cluster service detects component failures and responds by restarting resources or migrating resource groups.

• Restart resource in place if possible...

• ...else find another appropriate node and migrate/restart.

Ideally, migration/restart/failover is transparent.

• Logical resources (processes) execute in virtual environments.

uniform name space for files, registry, OS objects (NT mods)

• Node physical clocks are loosely synchronized, with clock drift less than minimal time for recovery/migration/restart.

guarantees migrated resource sees monotonically increasing clocks

• Route resource requests to the node hosting the resource.

• Is the failure visible to other resources that depend on the resource?

Membership 101Membership 101

Cluster nodes must agree on the set of cluster members (the view).

• distribute resource ownership effectively

shift resources on node failures or additions

• eliminate dangerous/expensive interactions with faulty nodes

• “keep everyone in the loop” on updates and events

e.g., multicast groups and group communication

The literature on group membership is tangled up with the problem of ordered multicast (e.g., “CATOCS”).

• What are the ordering guarantees for message delivery, especially with respect to membership changes?

• Ordered group communication is controversial, but everyone needs a solution for the separate but related membership problem.

Failure DetectorsFailure Detectors

First problem: how to detect that a member has failed?

• pings, timeouts, beacons, heartbeats

• recovery notifications

“I was gone for awhile, but now I’m back.”

Is the failure detector accurate?

Is the failure detector live?

In an asynchronous system, it is possible for a failure detector to be accurate or live, but not both.

• As it turns out, it is impossible for an asynchronous system to agree on anything with accuracy and liveness!

• But this is academic...

Failure Detectors in Real SystemsFailure Detectors in Real Systems

Common solution: • Use a failure detector that is live but not accurate.

Assume bounded processing delays and delivery times.

Timeout with multiple retries detects failure accurately with high probability.

If a “failed” site turns out to be alive, then kill it (fencing).

• Use a recovery detector that is accurate but not live.

“I’m back....hey, did anyone hear me?”

What do we assume about communication failures?How much pinging is enough?

1-to-N, N-to-N, ring?

What about network partitions?

Membership ServiceMembership Service

Second problem: How to propagate knowledge of failure/recovery events to other nodes?

• Surviving nodes should agree on the new view (regrouping).

• Convergence should be rapid.

• The regrouping protocol should itself be tolerant of message drops, message reorderings, and failures.

liveness and accuracy again

• The regrouping protocol should be scalable.

• The protocol should handle network partitions.

• Behavior of the messaging system (e.g., group multicast) across membership changes must be well-specified and understood.

Example: WombatExample: Wombat

• Wombat is a new membership protocol, an outgrowth of Porcupine.

Gretta Bartels, University of Washington, Duke ‘98

• Wombat is empirically more efficient/scalable than competing algorithms such as Three Round.

• But: Wombat makes no guarantees about the relative ordering of membership events and messages.

Adherents of group communication would not accept it as a “real” membership protocol.

• Wombat’s assumptions have not been formally defined, and its properties have not been proven.

If you can’t prove that it works, you can’t believe that it works.

• Disclaimer: Wombat is a promising work in progress.

Wombat BasicsWombat Basics

ping

ping

leader

minions

Nodes are ranked by unique IDs.

Node IDs are permanent.

Node i pings predecessor(i).

The highest-ranked node is the leader.

All other nodes are minions.

The leader periodically broadcasts its view to all known minions.

physical broadcast

Minions adopt the leader’s view.

determine pred from leader’s view

Node Arrival/Recovery in WombatNode Arrival/Recovery in Wombat

If node i joins the cluster:

1. i waits for the leader’s next beacon.

2. i detects that the leader’s view does not include i.

3. i notifies the leader.

4. The leader updates its view.

5. The leader broadcasts its new view.

6. Minions adopt the leader’s view.

“I’m here too.”

i

Node Failure in WombatNode Failure in Wombat

If a node fails:

1. Its successor notifies the leader.

2. The leader updates its view.

3. The leader broadcasts its view.

4. Minions adopt the leader’s new view.

5. Life goes on.

X

“Node i has failed.”

i

Leader Failure in WombatLeader Failure in Wombat

If the leader fails:

1. Successor detects the failure.

2. Successor knows that the failed node was the leader.

3. Successor broadcasts as leader.

4. Minions adopt the new leader’s view.

5. Life goes on.

X “I am in control.”

Multiple Failures in WombatMultiple Failures in Wombat

If the leader and its successor(s) fail(s), the next ranking node must assume command on its own.

1. Each node has a broadcast timer; if the timer goes off, broadcast as leader.

2. Each node’s timer is set by its rank.

if i< j then timer(i)<timer(j)

3. Reset timer on each beacon.

4. Leader’s timer value is adaptive.

Go faster if things are changing.

X

“I must be in control.”

X

Suppressing False LeadersSuppressing False Leaders

If a node falsely broadcasts as leader:

1. All nodes that know of a better leader recognize the usurper as such.

2. The real leader recognizes that it is a better leader than the usurper.

3. The real leader broadcasts the union of its view and the usurper’s view.

4. The usurper shuts up and adopts the real leader’s view.

What if the “real leader” is dead?

X

“I must be in control.”

“I don’t think so.”

Partitions in WombatPartitions in WombatpartitionleaderIf a network failure partitions the

cluster:

1. The old partition continues.

2. The leader of the new partition eventually broadcasts its view.

3. Minions accept the new leader’s view. partition

leader

notion

Healing a PartitionHealing a Partitiondominating

leaderWhen the partition heals, either:

1. The dominating partition leader hears a false broadcast, and...

2. ...corrects it by broadcasting the union of the views.

- or -

1. The dominating partition leader broadcasts first, and...

2. ...minions respond “I’m here”.

partitionleader

Wombat: WrinklesWombat: Wrinkles

1. What are the assumptions about:• network?• clocks?

2. Are these reasonable/realistic assumptions?

3. How to ensure a single cluster view in the event of a partition?

4. How long does it take for the view to converge after a partition?

5. How do we start a cluster? What if a node starts or recovers but never receives a beacon?

6. What about the ordering of messages and membership events?

7. How do minions come to accept a new leader?

8. What about “message storms”?